基于改进霜冰优化算法的短期光伏功率预测方法研究OA

[Objectives]Photovoltaic power generation is characterized by randomness,intermittency,and volatility.Its large-scale grid integration may impact power systems,thereby posing serious challenges to grid operation and scheduling.Therefore,developing a high-precision photovoltaic power prediction model and accurately characterizing the fluctuation patterns of photovoltaic power under varying meteorological conditions are of great significance for ensuring the stable operation of the power grid.To this end,this study proposes a photovoltaic power prediction method based on an intelligent optimization algorithm and a combined prediction model.[Methods]First,a residual network(ResNet)-LSTM-Dropout combined photovoltaic power prediction model is constructed with the long short-term memory network(LSTM)as the core model.In this model,the ResNet is used to extract deep nonlinear features from complex meteorological data,LSTM is employed to learn the temporal variation patterns of photovoltaic power sequences,and the Dropout layer is introduced to reduce the risk of overfitting during model training on complex samples,thereby improving the generalization performance of the model.Second,to address the problems that the rime optimization algorithm(RIME)easily falls into local optima and has slow convergence speed,the cosine strategy is introduced to improve local exploration capability,the role strategy is adopted to enhance global search capability,and the Cauchy mutation strategy is incorporated to avoid premature convergence.Accordingly,an improved rime optimization algorithm(IRIME)is proposed.Finally,IRIME is used to optimize the key hyperparameters of the ResNet-LSTM-Dropout combined model,and the optimized model is applied to short-term photovoltaic power prediction.[Results]The effectiveness of the proposed model is verified using measured data from a photovoltaic power station in Northwest China.The experimental results show that,under different weather conditions,the IRIME-ResNet-LSTM-Dropout model outperforms other comparison models in prediction performance,with more significant advantages under complex weather conditions such as cloudy and rainy days.[Conclusions]The proposed method effectively improves the accuracy of photovoltaic power prediction,providing important theoretical support for ensuring safe and stable grid operation and optimizing coordinated planning in power systems.